Merge remote-tracking branch 'origin/main' into WIP

run.py

@@ -36,6 +36,14 @@ def main():
         action='store_true',
         help='Continue running additional jobs even if a job fails'
     )
+
+    # flag to continue if failed job
+    parser.add_argument(
+        '-n', '--name',
+        type=str,
+        default=None,
+        help='Name to replace [name] tag in config file, useful for shared config file'
+    )
     args = parser.parse_args()
 
     config_file_list = args.config_file_list
@@ -49,7 +57,7 @@ def main():
 
     for config_file in config_file_list:
         try:
-            job = get_job(config_file)
+            job = get_job(config_file, args.name)
             job.run()
             job.cleanup()
             jobs_completed += 1

testing/compare_keys.py

@@ -0,0 +1,89 @@
+import argparse
+import os
+
+import torch
+from safetensors.torch import load_file
+from collections import OrderedDict
+import json
+# this was just used to match the vae keys to the diffusers keys
+# you probably wont need this. Unless they change them.... again... again
+# on second thought, you probably will
+
+device = torch.device('cpu')
+dtype = torch.float32
+
+parser = argparse.ArgumentParser()
+
+# require at lease one config file
+parser.add_argument(
+    'file_1',
+    nargs='+',
+    type=str,
+    help='Path to first safe tensor file'
+)
+
+parser.add_argument(
+    'file_2',
+    nargs='+',
+    type=str,
+    help='Path to second safe tensor file'
+)
+
+args = parser.parse_args()
+
+find_matches = False
+
+state_dict_file_1 = load_file(args.file_1[0])
+state_dict_1_keys = list(state_dict_file_1.keys())
+
+state_dict_file_2 = load_file(args.file_2[0])
+state_dict_2_keys = list(state_dict_file_2.keys())
+keys_in_both = []
+
+keys_not_in_state_dict_2 = []
+for key in state_dict_1_keys:
+    if key not in state_dict_2_keys:
+        keys_not_in_state_dict_2.append(key)
+
+keys_not_in_state_dict_1 = []
+for key in state_dict_2_keys:
+    if key not in state_dict_1_keys:
+        keys_not_in_state_dict_1.append(key)
+
+keys_in_both = []
+for key in state_dict_1_keys:
+    if key in state_dict_2_keys:
+        keys_in_both.append(key)
+
+# sort them
+keys_not_in_state_dict_2.sort()
+keys_not_in_state_dict_1.sort()
+keys_in_both.sort()
+
+
+json_data = {
+    "both": keys_in_both,
+    "state_dict_2": keys_not_in_state_dict_2,
+    "state_dict_1": keys_not_in_state_dict_1
+}
+json_data = json.dumps(json_data, indent=4)
+
+remaining_diffusers_values = OrderedDict()
+for key in keys_not_in_state_dict_1:
+    remaining_diffusers_values[key] = state_dict_file_2[key]
+
+# print(remaining_diffusers_values.keys())
+
+remaining_ldm_values = OrderedDict()
+for key in keys_not_in_state_dict_2:
+    remaining_ldm_values[key] = state_dict_file_1[key]
+
+# print(json_data)
+
+project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+json_save_path = os.path.join(project_root, 'config', 'keys.json')
+json_matched_save_path = os.path.join(project_root, 'config', 'matched.json')
+json_duped_save_path = os.path.join(project_root, 'config', 'duped.json')
+
+with open(json_save_path, 'w') as f:
+    f.write(json_data)

toolkit/config.py

@@ -15,22 +15,24 @@ def get_cwd_abs_path(path):
     return path
 
 
-def preprocess_config(config: OrderedDict):
+def preprocess_config(config: OrderedDict, name: str = None):
     if "job" not in config:
         raise ValueError("config file must have a job key")
     if "config" not in config:
         raise ValueError("config file must have a config section")
-    if "name" not in config["config"]:
+    if "name" not in config["config"] and name is None:
         raise ValueError("config file must have a config.name key")
     # we need to replace tags. For now just [name]
-    name = config["config"]["name"]
+    if name is not None:
+        config["config"]["name"] = name
+    else:
+        name = config["config"]["name"]
     config_string = json.dumps(config)
     config_string = config_string.replace("[name]", name)
     config = json.loads(config_string, object_pairs_hook=OrderedDict)
     return config
 
 
-
 # Fixes issue where yaml doesnt load exponents correctly
 fixed_loader = yaml.SafeLoader
 fixed_loader.add_implicit_resolver(
@@ -44,7 +46,8 @@ fixed_loader.add_implicit_resolver(
     |\\.(?:nan|NaN|NAN))$''', re.X),
     list(u'-+0123456789.'))
 
-def get_config(config_file_path):
+
+def get_config(config_file_path, name=None):
     # first check if it is in the config folder
     config_path = os.path.join(TOOLKIT_ROOT, 'config', config_file_path)
     # see if it is in the config folder with any of the possible extensions if it doesnt have one
@@ -75,4 +78,4 @@ def get_config(config_file_path):
     else:
         raise ValueError(f"Config file {config_file_path} must be a json or yaml file")
 
-    return preprocess_config(config)
+    return preprocess_config(config, name)

toolkit/job.py

@@ -1,8 +1,8 @@
 from toolkit.config import get_config
 
 
-def get_job(config_path):
-    config = get_config(config_path)
+def get_job(config_path, name=None):
+    config = get_config(config_path, name)
     if not config['job']:
         raise ValueError('config file is invalid. Missing "job" key')
 

toolkit/lycoris_utils.py

@@ -67,9 +67,6 @@ def extract_conv(
     return (extract_weight_A, extract_weight_B, diff), 'low rank'
 
 
-extra_weights = ['lora_unet_conv_in.alpha', 'lora_unet_conv_in.lora_down.weight', 'lora_unet_conv_in.lora_mid.weight', 'lora_unet_conv_in.lora_up.weight', 'lora_unet_conv_out.alpha', 'lora_unet_conv_out.lora_down.weight', 'lora_unet_conv_out.lora_mid.weight', 'lora_unet_conv_out.lora_up.weight', 'lora_unet_down_blocks_0_resnets_0_time_emb_proj.alpha', 'lora_unet_down_blocks_0_resnets_0_time_emb_proj.lora_down.weight', 'lora_unet_down_blocks_0_resnets_0_time_emb_proj.lora_up.weight', 'lora_unet_down_blocks_0_resnets_1_time_emb_proj.alpha', 'lora_unet_down_blocks_0_resnets_1_time_emb_proj.lora_down.weight', 'lora_unet_down_blocks_0_resnets_1_time_emb_proj.lora_up.weight', 'lora_unet_down_blocks_1_resnets_0_conv_shortcut.alpha', 'lora_unet_down_blocks_1_resnets_0_conv_shortcut.lora_down.weight', 'lora_unet_down_blocks_1_resnets_0_conv_shortcut.lora_up.weight', 'lora_unet_down_blocks_1_resnets_0_time_emb_proj.alpha', 'lora_unet_down_blocks_1_resnets_0_time_emb_proj.lora_down.weight', 'lora_unet_down_blocks_1_resnets_0_time_emb_proj.lora_up.weight', 'lora_unet_down_blocks_1_resnets_1_time_emb_proj.alpha', 'lora_unet_down_blocks_1_resnets_1_time_emb_proj.lora_down.weight', 'lora_unet_down_blocks_1_resnets_1_time_emb_proj.lora_up.weight', 'lora_unet_down_blocks_2_resnets_0_conv_shortcut.alpha', 'lora_unet_down_blocks_2_resnets_0_conv_shortcut.lora_down.weight', 'lora_unet_down_blocks_2_resnets_0_conv_shortcut.lora_up.weight', 'lora_unet_down_blocks_2_resnets_0_time_emb_proj.alpha', 'lora_unet_down_blocks_2_resnets_0_time_emb_proj.lora_down.weight', 'lora_unet_down_blocks_2_resnets_0_time_emb_proj.lora_up.weight', 'lora_unet_down_blocks_2_resnets_1_time_emb_proj.alpha', 'lora_unet_down_blocks_2_resnets_1_time_emb_proj.lora_down.weight', 'lora_unet_down_blocks_2_resnets_1_time_emb_proj.lora_up.weight', 'lora_unet_down_blocks_3_resnets_0_time_emb_proj.alpha', 'lora_unet_down_blocks_3_resnets_0_time_emb_proj.lora_down.weight', 'lora_unet_down_blocks_3_resnets_0_time_emb_proj.lora_up.weight', 'lora_unet_down_blocks_3_resnets_1_time_emb_proj.alpha', 'lora_unet_down_blocks_3_resnets_1_time_emb_proj.lora_down.weight', 'lora_unet_down_blocks_3_resnets_1_time_emb_proj.lora_up.weight', 'lora_unet_mid_block_resnets_0_time_emb_proj.alpha', 'lora_unet_mid_block_resnets_0_time_emb_proj.lora_down.weight', 'lora_unet_mid_block_resnets_0_time_emb_proj.lora_up.weight', 'lora_unet_mid_block_resnets_1_time_emb_proj.alpha', 'lora_unet_mid_block_resnets_1_time_emb_proj.lora_down.weight', 'lora_unet_mid_block_resnets_1_time_emb_proj.lora_up.weight', 'lora_unet_time_embedding_linear_1.alpha', 'lora_unet_time_embedding_linear_1.lora_down.weight', 'lora_unet_time_embedding_linear_1.lora_up.weight', 'lora_unet_time_embedding_linear_2.alpha', 'lora_unet_time_embedding_linear_2.lora_down.weight', 'lora_unet_time_embedding_linear_2.lora_up.weight', 'lora_unet_up_blocks_0_resnets_0_conv_shortcut.alpha', 'lora_unet_up_blocks_0_resnets_0_conv_shortcut.lora_down.weight', 'lora_unet_up_blocks_0_resnets_0_conv_shortcut.lora_up.weight', 'lora_unet_up_blocks_0_resnets_0_time_emb_proj.alpha', 'lora_unet_up_blocks_0_resnets_0_time_emb_proj.lora_down.weight', 'lora_unet_up_blocks_0_resnets_0_time_emb_proj.lora_up.weight', 'lora_unet_up_blocks_0_resnets_1_conv_shortcut.alpha', 'lora_unet_up_blocks_0_resnets_1_conv_shortcut.lora_down.weight', 'lora_unet_up_blocks_0_resnets_1_conv_shortcut.lora_up.weight', 'lora_unet_up_blocks_0_resnets_1_time_emb_proj.alpha', 'lora_unet_up_blocks_0_resnets_1_time_emb_proj.lora_down.weight', 'lora_unet_up_blocks_0_resnets_1_time_emb_proj.lora_up.weight', 'lora_unet_up_blocks_0_resnets_2_conv_shortcut.alpha', 'lora_unet_up_blocks_0_resnets_2_conv_shortcut.lora_down.weight', 'lora_unet_up_blocks_0_resnets_2_conv_shortcut.lora_up.weight', 'lora_unet_up_blocks_0_resnets_2_time_emb_proj.alpha', 'lora_unet_up_blocks_0_resnets_2_time_emb_proj.lora_down.weight', 'lora_unet_up_blocks_0_resnets_2_time_emb_proj.lora_up.weight', 'lora_unet_up_blocks_1_resnets_0_conv_shortcut.alpha', 'lora_unet_up_blocks_1_resnets_0_conv_shortcut.lora_down.weight', 'lora_unet_up_blocks_1_resnets_0_conv_shortcut.lora_up.weight', 'lora_unet_up_blocks_1_resnets_0_time_emb_proj.alpha', 'lora_unet_up_blocks_1_resnets_0_time_emb_proj.lora_down.weight', 'lora_unet_up_blocks_1_resnets_0_time_emb_proj.lora_up.weight', 'lora_unet_up_blocks_1_resnets_1_conv_shortcut.alpha', 'lora_unet_up_blocks_1_resnets_1_conv_shortcut.lora_down.weight', 'lora_unet_up_blocks_1_resnets_1_conv_shortcut.lora_up.weight', 'lora_unet_up_blocks_1_resnets_1_time_emb_proj.alpha', 'lora_unet_up_blocks_1_resnets_1_time_emb_proj.lora_down.weight', 'lora_unet_up_blocks_1_resnets_1_time_emb_proj.lora_up.weight', 'lora_unet_up_blocks_1_resnets_2_conv_shortcut.alpha', 'lora_unet_up_blocks_1_resnets_2_conv_shortcut.lora_down.weight', 'lora_unet_up_blocks_1_resnets_2_conv_shortcut.lora_up.weight', 'lora_unet_up_blocks_1_resnets_2_time_emb_proj.alpha', 'lora_unet_up_blocks_1_resnets_2_time_emb_projLine truncated
-
-
 def extract_linear(
         weight: Union[torch.Tensor, nn.Parameter],
         mode='fixed',
@@ -177,7 +174,7 @@ def extract_diff(
             if module.__class__.__name__ in target_replace_modules:
                 temp[name] = {}
                 for child_name, child_module in module.named_modules():
-                    if child_module.__class__.__name__ not in {'Linear', 'Conv2d'}:
+                    if child_module.__class__.__name__ not in {'Linear', 'LoRACompatibleLinear', 'Conv2d', 'LoRACompatibleConv'}:
                         continue
                     temp[name][child_name] = child_module.weight
             elif name in target_replace_names:
@@ -190,12 +187,12 @@ def extract_diff(
                     lora_name = prefix + '.' + name + '.' + child_name
                     lora_name = lora_name.replace('.', '_')
                     layer = child_module.__class__.__name__
-                    if layer in {'Linear', 'Conv2d'}:
+                    if layer in {'Linear', 'LoRACompatibleLinear', 'Conv2d', 'LoRACompatibleConv'}:
                         root_weight = child_module.weight
                         if torch.allclose(root_weight, weights[child_name]):
                             continue
 
-                    if layer == 'Linear':
+                    if layer == 'Linear' or layer == 'LoRACompatibleLinear':
                         weight, decompose_mode = extract_linear(
                             (child_module.weight - weights[child_name]),
                             mode,
@@ -204,7 +201,7 @@ def extract_diff(
                         )
                         if decompose_mode == 'low rank':
                             extract_a, extract_b, diff = weight
-                    elif layer == 'Conv2d':
+                    elif layer == 'Conv2d' or layer == 'LoRACompatibleConv':
                         is_linear = (child_module.weight.shape[2] == 1
                                      and child_module.weight.shape[3] == 1)
                         if not is_linear and linear_only:
@@ -258,12 +255,12 @@ def extract_diff(
                 lora_name = lora_name.replace('.', '_')
                 layer = module.__class__.__name__
 
-                if layer in {'Linear', 'Conv2d'}:
+                if layer in {'Linear', 'LoRACompatibleLinear', 'Conv2d', 'LoRACompatibleConv'}:
                     root_weight = module.weight
                     if torch.allclose(root_weight, weights):
                         continue
 
-                if layer == 'Linear':
+                if layer == 'Linear' or layer == 'LoRACompatibleLinear':
                     weight, decompose_mode = extract_linear(
                         (root_weight - weights),
                         mode,
@@ -272,7 +269,7 @@ def extract_diff(
                     )
                     if decompose_mode == 'low rank':
                         extract_a, extract_b, diff = weight
-                elif layer == 'Conv2d':
+                elif layer == 'Conv2d' or layer == 'LoRACompatibleConv':
                     is_linear = (
                             root_weight.shape[2] == 1
                             and root_weight.shape[3] == 1
@@ -493,7 +490,8 @@ def merge(
         for name, module in tqdm(list(root_module.named_modules()), desc=f'Merging {prefix}'):
             if module.__class__.__name__ in target_replace_modules:
                 for child_name, child_module in module.named_modules():
-                    if child_module.__class__.__name__ not in {'Linear', 'Conv2d'}:
+                    if child_module.__class__.__name__ not in {'Linear', 'LoRACompatibleLinear', 'Conv2d',
+                                                               'LoRACompatibleConv'}:
                         continue
                     lora_name = prefix + '.' + name + '.' + child_name
                     lora_name = lora_name.replace('.', '_')